Using motion capture technology and data from wearable devices, North Carolina State University researchers developed an energy-efficient way of tracking a user's physical activity.

“Tracking physical activity is important because it is a key component for placing other health data in context,” says Edgar Lobaton, an assistant professor of electrical and computer engineering at NC State and senior author of a paper on the new work. “For example, a spike in heart rate is normal when exercising, but can be an indicator of health problems in other circumstances.”

When monitoring physical activity, the program must determine how much data to process. For example, looking at all of the data collected over a 10-second increment, or tau, takes twice as much computing power as evaluating all of the data over a five-second tau.

To address the efficiency challenge, graduate students performed five different activities in a motion-capture lab: golfing, biking, walking, waving, and sitting.

Using a mathematical technique called topological persistence analysis, the NC State team's clustering methodology captured essential hierarchies and dependencies in the data set.

“Based on this specific set of experimental data, we found that we could accurately identify the five relevant activities using a tau of six seconds,” Lobaton says. “This means we could identify activities and store related data efficiently."

Lobaton and researchers are hopeful that the approach offers the best opportunity to track and record physical activity data in a practical way, providing meaningful information to users of wearable health monitoring devices.